*2.4. Data Analysis*

Peptide identification from raw data was carried out using the PEAKS Studio X search engine (Bioinformatics Solutions Inc, Waterloo, ON, Canada). A custom Python script was used to create a database comprising all possible open reading frames (ORF) coding for protein sequences of ≥20 amino acids existing in any of the six-frames in the *L. infantum* JPCM5 strain genome sequence [13]. This database (named LINF-all-ORFs) consisted of 294,654 entries. In parallel, a fusion-database, created by merging the *L. infantum* protein sequences annotated in UniProt and the LINF-all-ORFs entries, was also used by the search engine. Finally, a search against a decoy database (decoy fusion-database) was also performed. The following constraints were used for the searches: tryptic or chymotryptic cleavage (semispecific), up to two missed cleavage sites, tolerances of 20 ppm for precursor ions and 0.6 Da for MS/MS fragment ions, and optional Met oxidation and Cys carbamidomethylation were allowed. The false discovery rates (FDRs) for peptide spectrum matches

(PSMs) were limited to 0.01 or lower. Those proteins that were identified with at least two distinct peptides were considered for further analysis [27–29].

The LINF-all-ORFs entries with mapped peptides were compared with the annotated *L. infantum* proteins (UniProt database) using in-house Python scripts in order to identify both the misannotations and novel proteins. Additionally, Python scripts were used to ascribe post-translational modifications to particular protein entries.

Functional categories and enzymatic pathways using the DAVID program (Functional Annotation Tool, DAVID Bioinformatics Resources 6.8) and the KEGG Pathway (Kyoto Encyclopedia of Genes and Genomes) were used for the classification of the proteins identified by MS.
